class_docstr: This ``Module`` is a maxout neural network.
  
  Parameters
  ----------
  input_dim : ``int``
      The dimensionality of the input.  We assume the input has shape ``(batch_size, input_dim)``.
  num_layers : ``int``
      The number of maxout layers to apply to the input.
  output_dims : ``Union[int, Sequence[int]]``
      The output dimension of each of the maxout layers.  If this is a single ``int``, we use
      it for all maxout layers.  If it is a ``Sequence[int]``, ``len(output_dims)`` must be
      ``num_layers``.
  pool_sizes : ``Union[int, Sequence[int]]``
      The size of max-pools.  If this is a single ``int``, we use
      it for all maxout layers.  If it is a ``Sequence[int]``, ``len(pool_sizes)`` must be
      ``num_layers``.
  dropout : ``Union[float, Sequence[float]]``, optional
      If given, we will apply this amount of dropout after each layer.  Semantics of ``float``
      versus ``Sequence[float]`` is the same as with other parameters.
code: def forward(self, inputs):
      # pylint: disable=arguments-differ
      output = inputs
      for layer, layer_output_dim, dropout, pool_size in izip(
          self._linear_layers,
          self._output_dims,
          self._dropout,
          self._pool_sizes,
      ):
          affine_output = layer(output)
          # Compute and apply the proper shape for the max.
          shape = list(inputs.size())
          shape[-1] = layer_output_dim
          shape.append(pool_size)
  
          maxed_output = torch.max(affine_output.view(*shape), dim=-1)[
              0
          ]
          dropped_output = dropout(maxed_output)
          output = dropped_output
      return output
  
file_path: magnitude/pymagnitude/third_party/allennlp/modules/maxout.py
imports: from __future__ import absolute_import
  import torch
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.registrable import FromParams
label_desc: Use image node_id 4 for calling the Maxout obj's underlying member method code with example usage: obj.forward(inputs) and returns: output